Advances in Applied Energy最新文献

Reinforcement learning for vehicle-to-grid: A review

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-02-08 DOI: 10.1016/j.adapen.2025.100214

Hongbin Xie , Ge Song , Zhuoran Shi , Jingyuan Zhang , Zhenjia Lin , Qing Yu , Hongdi Fu , Xuan Song , Haoran Zhang

The rapid development of Vehicle-to-Grid technology has played a crucial role in peak shaving and power scheduling within the power grid. However, with the random integration of a large number of electric vehicles into the grid, the uncertainty and complexity of the system have significantly increased, posing substantial challenges to traditional algorithms. Reinforcement learning has shown great potential in addressing these high-dimensional dynamic scheduling optimization problems. However, there is currently a lack of comprehensive analysis and systematic understanding of reinforcement learning applications in Vehicle-to-Grid, which limits the further development of this technology in the Vehicle-to-Grid domain. To this end, this review systematically analyzes the application of reinforcement learning in Vehicle-to-Grid from the perspective of different stakeholders, including the power grid, aggregators, and electric vehicle users, and clarifies the effectiveness and mechanisms of reinforcement learning in addressing the uncertainty in power scheduling. Based on a comprehensive review of the development trajectory of reinforcement learning in Vehicle-to-Grid applications, this paper proposes a structured framework for method classification and application analysis. It also highlights the major challenges currently faced by reinforcement learning in the Vehicle-to-Grid domain and provides targeted directions for future research. Through this systematic review of reinforcement learning applications in Vehicle-to-Grid, the paper aims to provide relevant references for subsequent studies.

{"title":"Reinforcement learning for vehicle-to-grid: A review","authors":"Hongbin Xie , Ge Song , Zhuoran Shi , Jingyuan Zhang , Zhenjia Lin , Qing Yu , Hongdi Fu , Xuan Song , Haoran Zhang","doi":"10.1016/j.adapen.2025.100214","DOIUrl":"10.1016/j.adapen.2025.100214","url":null,"abstract":"<div><div>The rapid development of Vehicle-to-Grid technology has played a crucial role in peak shaving and power scheduling within the power grid. However, with the random integration of a large number of electric vehicles into the grid, the uncertainty and complexity of the system have significantly increased, posing substantial challenges to traditional algorithms. Reinforcement learning has shown great potential in addressing these high-dimensional dynamic scheduling optimization problems. However, there is currently a lack of comprehensive analysis and systematic understanding of reinforcement learning applications in Vehicle-to-Grid, which limits the further development of this technology in the Vehicle-to-Grid domain. To this end, this review systematically analyzes the application of reinforcement learning in Vehicle-to-Grid from the perspective of different stakeholders, including the power grid, aggregators, and electric vehicle users, and clarifies the effectiveness and mechanisms of reinforcement learning in addressing the uncertainty in power scheduling. Based on a comprehensive review of the development trajectory of reinforcement learning in Vehicle-to-Grid applications, this paper proposes a structured framework for method classification and application analysis. It also highlights the major challenges currently faced by reinforcement learning in the Vehicle-to-Grid domain and provides targeted directions for future research. Through this systematic review of reinforcement learning applications in Vehicle-to-Grid, the paper aims to provide relevant references for subsequent studies.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100214"},"PeriodicalIF":13.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancements and future outlook of Artificial Intelligence in energy and climate change modeling

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-28 DOI: 10.1016/j.adapen.2025.100211

Mobolaji Shobanke, Mehul Bhatt, Ekundayo Shittu

This paper explores the employment of artificial intelligence and machine learning to decipher strategic responses to incidences of climate change and to inform the management of energy systems. Given the increasing global dependence on sustainable and efficient energy solutions and the rise of artificial intelligence and machine learning, it has become imperative to evaluate existing routines in energy and climate change modeling to identify areas for further application. The process of conducting a systematic review of the contemporary literature highlights significant advances in optimization and predictive analytics within energy and climate change modeling systems driven by artificial intelligence and machine learning. This paper contributes to cutting-edge research on energy innovation, i.e., through the examination of the applications of artificial intelligence and machine learning in energy modeling and climate change assessments. The article bridges the gaps between research, development, and implementation with significant insights into the broader applications of artificial intelligence and machine learning in the analysis of future energy transitions and climate change mitigation and adaptation.

{"title":"Advancements and future outlook of Artificial Intelligence in energy and climate change modeling","authors":"Mobolaji Shobanke, Mehul Bhatt, Ekundayo Shittu","doi":"10.1016/j.adapen.2025.100211","DOIUrl":"10.1016/j.adapen.2025.100211","url":null,"abstract":"<div><div>This paper explores the employment of artificial intelligence and machine learning to decipher strategic responses to incidences of climate change and to inform the management of energy systems. Given the increasing global dependence on sustainable and efficient energy solutions and the rise of artificial intelligence and machine learning, it has become imperative to evaluate existing routines in energy and climate change modeling to identify areas for further application. The process of conducting a systematic review of the contemporary literature highlights significant advances in optimization and predictive analytics within energy and climate change modeling systems driven by artificial intelligence and machine learning. This paper contributes to cutting-edge research on energy innovation, <em>i.e.</em>, through the examination of the applications of artificial intelligence and machine learning in energy modeling and climate change assessments. The article bridges the gaps between research, development, and implementation with significant insights into the broader applications of artificial intelligence and machine learning in the analysis of future energy transitions and climate change mitigation and adaptation.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100211"},"PeriodicalIF":13.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing building facade solar potential assessment through AIoT, GIS, and meteorology synergy

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-27 DOI: 10.1016/j.adapen.2025.100212

Kechuan Dong , Qing Yu , Zhiling Guo , Jian Xu , Hongjun Tan , Haoran Zhang , Jinyue Yan

The assessment of building solar potential plays a pivotal role in Building Integrated Photovoltaics (BIPV) and urban energy systems. While current evaluations predominantly focus on rooftop solar resources, a comprehensive analysis of building facade BIPV potential is often lacking. This study presents an innovative methodology that harnesses state-of-the-art Artificial Intelligence of Things (AIoT) techniques, Geographic Information Systems (GIS), and Meteorology to develop a model for accurately estimating spatial–temporal building facade BIPV potential considering 3 Dimension (3D) shading effect. Here, we introduce a zero-shot Deep Learning framework for detailed parsing of facade elements, utilizing cutting-edge techniques in Large-scale Segment Anything Model (SAM), Grounding DINO (Detection Transformer with improved denoising anchor boxes), and Stable Diffusion. Considering urban morphology, 3D shading impacts, and multi-source weather data enables a meticulous estimation of solar potential for each facade element. The experimental findings, gathered from a range of buildings across four countries and an entire street in Japan, highlight the effectiveness and applicability of our approach in conducting comprehensive analyses of facade solar potential. These results underscore the critical importance of integrating shadow effects and detailed facade elements to ensure accurate estimations of PV potential.

{"title":"Advancing building facade solar potential assessment through AIoT, GIS, and meteorology synergy","authors":"Kechuan Dong , Qing Yu , Zhiling Guo , Jian Xu , Hongjun Tan , Haoran Zhang , Jinyue Yan","doi":"10.1016/j.adapen.2025.100212","DOIUrl":"10.1016/j.adapen.2025.100212","url":null,"abstract":"<div><div>The assessment of building solar potential plays a pivotal role in Building Integrated Photovoltaics (BIPV) and urban energy systems. While current evaluations predominantly focus on rooftop solar resources, a comprehensive analysis of building facade BIPV potential is often lacking. This study presents an innovative methodology that harnesses state-of-the-art Artificial Intelligence of Things (AIoT) techniques, Geographic Information Systems (GIS), and Meteorology to develop a model for accurately estimating spatial–temporal building facade BIPV potential considering 3 Dimension (3D) shading effect. Here, we introduce a zero-shot Deep Learning framework for detailed parsing of facade elements, utilizing cutting-edge techniques in Large-scale Segment Anything Model (SAM), Grounding DINO (Detection Transformer with improved denoising anchor boxes), and Stable Diffusion. Considering urban morphology, 3D shading impacts, and multi-source weather data enables a meticulous estimation of solar potential for each facade element. The experimental findings, gathered from a range of buildings across four countries and an entire street in Japan, highlight the effectiveness and applicability of our approach in conducting comprehensive analyses of facade solar potential. These results underscore the critical importance of integrating shadow effects and detailed facade elements to ensure accurate estimations of PV potential.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100212"},"PeriodicalIF":13.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating water availability for electrolysis into energy system modeling

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-27 DOI: 10.1016/j.adapen.2025.100208

Julian Walter , Lina Fischer , Sandra Venghaus , Albert Moser

In recent years, temperature records have been broken all over the world and the global temperature keeps rising. As a result, fresh water availability will diminish ever more and more due to droughts and extreme weather events. Water is a key part of many central aspects of life but will also become important in the future for electrolysis to synthesize hydrogen, a promising energy carrier in energy systems for the transition from fossil to renewable energy. Current energy system optimization models neglect water as an input for electrolysis when focusing on electricity. In this study, we present a method for implementing water as an input in energy system optimization models, with constraints for freshwater availability and seawater processing. We apply our method to one scenario and investigate the impact on the European energy system with highly-detailed spatial and temporal resolutions. The results indicate a relocation of electrolysis capacities of 10% and an increase of methane imports and methanation capacities. The effects suggest that water should be considered in energy system optimization in the future.

{"title":"Integrating water availability for electrolysis into energy system modeling","authors":"Julian Walter , Lina Fischer , Sandra Venghaus , Albert Moser","doi":"10.1016/j.adapen.2025.100208","DOIUrl":"10.1016/j.adapen.2025.100208","url":null,"abstract":"<div><div>In recent years, temperature records have been broken all over the world and the global temperature keeps rising. As a result, fresh water availability will diminish ever more and more due to droughts and extreme weather events. Water is a key part of many central aspects of life but will also become important in the future for electrolysis to synthesize hydrogen, a promising energy carrier in energy systems for the transition from fossil to renewable energy. Current energy system optimization models neglect water as an input for electrolysis when focusing on electricity. In this study, we present a method for implementing water as an input in energy system optimization models, with constraints for freshwater availability and seawater processing. We apply our method to one scenario and investigate the impact on the European energy system with highly-detailed spatial and temporal resolutions. The results indicate a relocation of electrolysis capacities of 10% and an increase of methane imports and methanation capacities. The effects suggest that water should be considered in energy system optimization in the future.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100208"},"PeriodicalIF":13.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of participatory modelling techniques for energy transition scenarios

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-26 DOI: 10.1016/j.adapen.2025.100215

Jair K.E.K. Campfens , Mert Duygan , Claudia R. Binder

Energy transitions are pivotal for sustainability, yet their complexity and uncertainty pose significant challenges for effective planning and implementation. Participatory modelling has emerged as a promising approach to support these transitions, as it involves incorporating stakeholders' perspectives into models and policy designs, which helps integrate their mental models and preferences into simulations. This paper reviews the current state of participatory modelling in transition research for energy scenarios. Drawing on a comprehensive literature review and semi-structured interviews, we extract findings by evaluating participatory modelling techniques against criteria such as normative dimensions, non-linearity, actors and agency, uncertainty and emergence. Findings reveal that techniques like Cross-Impact Balance analysis and Fuzzy Cognitive Mapping excel in incorporating normative aspects and capturing diverse actor perspectives, yet they face challenges in addressing non-linearity and uncertainty. Bayesian Networks and Agent-Based Models are strong in managing uncertainty and modelling emergent behaviours but show limitations in normative aspects. Our findings provide a foundation for scholars and practitioners in the field of socio-technical energy transitions to select participatory modelling techniques best suited to their specific research contexts. This review also highlights gaps between theoretical potential and practical application of participatory modelling techniques. Bridging these gaps requires methodological advancement and a more rigorous application in empirical studies. To this end, future directions for blending techniques are discussed to better address the complexities of energy transitions.

{"title":"A review of participatory modelling techniques for energy transition scenarios","authors":"Jair K.E.K. Campfens , Mert Duygan , Claudia R. Binder","doi":"10.1016/j.adapen.2025.100215","DOIUrl":"10.1016/j.adapen.2025.100215","url":null,"abstract":"<div><div>Energy transitions are pivotal for sustainability, yet their complexity and uncertainty pose significant challenges for effective planning and implementation. Participatory modelling has emerged as a promising approach to support these transitions, as it involves incorporating stakeholders' perspectives into models and policy designs, which helps integrate their mental models and preferences into simulations. This paper reviews the current state of participatory modelling in transition research for energy scenarios. Drawing on a comprehensive literature review and semi-structured interviews, we extract findings by evaluating participatory modelling techniques against criteria such as normative dimensions, non-linearity, actors and agency, uncertainty and emergence. Findings reveal that techniques like Cross-Impact Balance analysis and Fuzzy Cognitive Mapping excel in incorporating normative aspects and capturing diverse actor perspectives, yet they face challenges in addressing non-linearity and uncertainty. Bayesian Networks and Agent-Based Models are strong in managing uncertainty and modelling emergent behaviours but show limitations in normative aspects. Our findings provide a foundation for scholars and practitioners in the field of socio-technical energy transitions to select participatory modelling techniques best suited to their specific research contexts. This review also highlights gaps between theoretical potential and practical application of participatory modelling techniques. Bridging these gaps requires methodological advancement and a more rigorous application in empirical studies. To this end, future directions for blending techniques are discussed to better address the complexities of energy transitions.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100215"},"PeriodicalIF":13.0,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive reinforcement learning for energy management – A progressive approach to boost climate resilience and energy flexibility

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-22 DOI: 10.1016/j.adapen.2025.100213

Vahid M. Nik , Kavan Javanroodi

Energy management in urban areas is challenging due to diverse energy users, dynamics environmental conditions, and the added complexity and instability of extreme weather events. We incorporate adaptive reinforcement learning (ARL) into energy management (EM) and introduce a novel approach, called ARLEM. An online, value-based, model-free ARL engine is designed that updates its policy periodically and partially by replacing less favorable actions with those better adapted to evolving environmental conditions. Multiple policy update mechanisms are assessed, varying based on the frequency and length of updates and the action selection criteria. ARLEM is tested to control the energy performance of typical urban blocks in Madrid and Stockholm considering 17 future climate scenarios for 2040–2069. Each block contains 24 buildings of different types and ages. In Madrid, ARLEM is tested for a summer with two heatwaves and in Stockholm for a winter with two cold waves. Three performance indicators are defined to evaluate the effectiveness and resilience of different control approaches during extreme weather events. ARLEM demonstrates an ability to increase climate resilience in the studied blocks by increasing energy flexibility in the network and reducing both average and peak energy demands while affecting indoor thermal comfort marginally. Since the approach does not require any information about the system dynamics, it is easy to cope with the complexities of building systems and technologies, making it an affordable technology to control large urban areas with diverse types of buildings.

{"title":"Adaptive reinforcement learning for energy management – A progressive approach to boost climate resilience and energy flexibility","authors":"Vahid M. Nik , Kavan Javanroodi","doi":"10.1016/j.adapen.2025.100213","DOIUrl":"10.1016/j.adapen.2025.100213","url":null,"abstract":"<div><div>Energy management in urban areas is challenging due to diverse energy users, dynamics environmental conditions, and the added complexity and instability of extreme weather events. We incorporate adaptive reinforcement learning (ARL) into energy management (EM) and introduce a novel approach, called ARLEM. An online, value-based, model-free ARL engine is designed that updates its policy periodically and partially by replacing less favorable actions with those better adapted to evolving environmental conditions. Multiple policy update mechanisms are assessed, varying based on the frequency and length of updates and the action selection criteria. ARLEM is tested to control the energy performance of typical urban blocks in Madrid and Stockholm considering 17 future climate scenarios for 2040–2069. Each block contains 24 buildings of different types and ages. In Madrid, ARLEM is tested for a summer with two heatwaves and in Stockholm for a winter with two cold waves. Three performance indicators are defined to evaluate the effectiveness and resilience of different control approaches during extreme weather events. ARLEM demonstrates an ability to increase climate resilience in the studied blocks by increasing energy flexibility in the network and reducing both average and peak energy demands while affecting indoor thermal comfort marginally. Since the approach does not require any information about the system dynamics, it is easy to cope with the complexities of building systems and technologies, making it an affordable technology to control large urban areas with diverse types of buildings.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100213"},"PeriodicalIF":13.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding bidding strategies of intermittent renewables in negative price environments: A theoretical and empirical analysis

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-15 DOI: 10.1016/j.adapen.2025.100209

Qinghu Tang , Hongye Guo , Daniel S. Kirschen , Chongqing Kang

Negative electricity prices have become increasingly prevalent with the growing penetration of intermittent renewable energy sources worldwide. Although it is widely thought that the negative prices are primarily driven by intermittent renewable energies, the bidding decision theory behind this phenomenon remains underexplored. This paper seeks to illuminate the bidding theory of intermittent renewables under negative electricity prices through not only a theoretical model but also an empirical analysis of its real-world counterpart. First, we propose a comprehensive intermittent renewable bidding decision model considering both forward contract and spot market, as well as income from both the energy market and green energy incentive, which significantly influence bidding behavior under negative price conditions. Next, we develop a data-driven approach to estimate the model’s embedded parameters using publicly available market data, enabling direct comparison with real-world counterparts. Finally, on the basis of the proposed model, we analyze the actual bid records in comparison to the optimal bidding decisions from three perspectives: strategy, behavior, and profit. Empirical results show that the proposed model can explain 80% of the bidding strategies employed by intermittent renewable power plants in a real-world market, including suboptimal strategies. Furthermore, some empirical evidence can help understand the intrinsic relationship between bidding rationality and negative price severity.

{"title":"Understanding bidding strategies of intermittent renewables in negative price environments: A theoretical and empirical analysis","authors":"Qinghu Tang , Hongye Guo , Daniel S. Kirschen , Chongqing Kang","doi":"10.1016/j.adapen.2025.100209","DOIUrl":"10.1016/j.adapen.2025.100209","url":null,"abstract":"<div><div>Negative electricity prices have become increasingly prevalent with the growing penetration of intermittent renewable energy sources worldwide. Although it is widely thought that the negative prices are primarily driven by intermittent renewable energies, the bidding decision theory behind this phenomenon remains underexplored. This paper seeks to illuminate the bidding theory of intermittent renewables under negative electricity prices through not only a theoretical model but also an empirical analysis of its real-world counterpart. First, we propose a comprehensive intermittent renewable bidding decision model considering both forward contract and spot market, as well as income from both the energy market and green energy incentive, which significantly influence bidding behavior under negative price conditions. Next, we develop a data-driven approach to estimate the model’s embedded parameters using publicly available market data, enabling direct comparison with real-world counterparts. Finally, on the basis of the proposed model, we analyze the actual bid records in comparison to the optimal bidding decisions from three perspectives: strategy, behavior, and profit. Empirical results show that the proposed model can explain 80% of the bidding strategies employed by intermittent renewable power plants in a real-world market, including suboptimal strategies. Furthermore, some empirical evidence can help understand the intrinsic relationship between bidding rationality and negative price severity.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100209"},"PeriodicalIF":13.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A newly developed spatially resolved modelling framework for hydrogen valleys: Methodology and functionality

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-05 DOI: 10.1016/j.adapen.2025.100207

Friedrich Mendler , Christopher Voglstätter , Nikolas Müller , Tom Smolinka , Marius Holst , Christopher Hebling , Barbara Koch

Regional initiatives, like the European hydrogen valleys, aim to solve the simultaneous absence of green hydrogen production, infrastructure, and application with coordinated development of the whole supply chain. A new model framework was developed to bridge the gap between linearised energy system models and detailed plant simulations that allows for dynamic, nonlinear simulation and optimisation of regional hydrogen systems from electricity generation to hydrogen application. The model incorporates different supply algorithms for electricity and hydrogen, representing both bilateral contracts and flexible markets. A case study demonstrates the application of the framework within a representative hydrogen valley in Germany, showing how the model can identify optimal configurations of hydrogen production, storage, and distribution infrastructure to minimise the levelized cost of hydrogen. The influence of different spatial resolutions, exchange control algorithms, and boundary conditions chain are evaluated. A too coarse spatial resolution can underestimate system cost by up to 10 % while the allowance of both bilateral hydrogen contracts and a flexible market algorithm can increase hydrogen utilisation and reduce cost by up to 15 %. An autarkic supply of hydrogen demands was possible for 7.60 €/kg, while the option to use grid electricity reduces costs to 6.37 €/kg and the option to import hydrogen to 6.60 €/kg, based on the assumptions for electricity and hydrogen prices. This work contributes to the evolving field of hydrogen economy by providing a sophisticated tool for policymakers and industry stakeholders worldwide to plan and optimise regional hydrogen valleys effectively.

{"title":"A newly developed spatially resolved modelling framework for hydrogen valleys: Methodology and functionality","authors":"Friedrich Mendler , Christopher Voglstätter , Nikolas Müller , Tom Smolinka , Marius Holst , Christopher Hebling , Barbara Koch","doi":"10.1016/j.adapen.2025.100207","DOIUrl":"10.1016/j.adapen.2025.100207","url":null,"abstract":"<div><div>Regional initiatives, like the European hydrogen valleys, aim to solve the simultaneous absence of green hydrogen production, infrastructure, and application with coordinated development of the whole supply chain. A new model framework was developed to bridge the gap between linearised energy system models and detailed plant simulations that allows for dynamic, nonlinear simulation and optimisation of regional hydrogen systems from electricity generation to hydrogen application. The model incorporates different supply algorithms for electricity and hydrogen, representing both bilateral contracts and flexible markets. A case study demonstrates the application of the framework within a representative hydrogen valley in Germany, showing how the model can identify optimal configurations of hydrogen production, storage, and distribution infrastructure to minimise the levelized cost of hydrogen. The influence of different spatial resolutions, exchange control algorithms, and boundary conditions chain are evaluated. A too coarse spatial resolution can underestimate system cost by up to 10 % while the allowance of both bilateral hydrogen contracts and a flexible market algorithm can increase hydrogen utilisation and reduce cost by up to 15 %. An autarkic supply of hydrogen demands was possible for 7.60 €/kg, while the option to use grid electricity reduces costs to 6.37 €/kg and the option to import hydrogen to 6.60 €/kg, based on the assumptions for electricity and hydrogen prices. This work contributes to the evolving field of hydrogen economy by providing a sophisticated tool for policymakers and industry stakeholders worldwide to plan and optimise regional hydrogen valleys effectively.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100207"},"PeriodicalIF":13.0,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143104643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boosting direct-ethane solid oxide fuel cell efficiency with anchored palladium nanoparticles on perovskite-based anode

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-05 DOI: 10.1016/j.adapen.2025.100206

Shuo Zhai , Junyu Cai , Idris Temitope Bello , Xi Chen , Na Yu , Rubao Zhao , Xingke Cai , Yunhong Jiang , Meng Ni , Heping Xie

An efficient anode catalyst for hydrocarbon fuel in Solid Oxide Fuel Cells (SOFC) should possess a stable phase structure, high catalytic efficiency, and excellent coke resistance. However, traditional nickel-based anodes necessitate high steam-to-carbon ratios to prevent coking, complicating system design and reducing the overall performance. In this work, we report a nickel-free PrBaFe_1.9Pd_0.1O_5+δ perovskite as anode material for direct ethane SOFC, which demonstrates superior electroactivity and chemical stability. Under a reducing atmosphere, Pd nano-catalysts exsolved in-situ are uniformly anchored to the perovskite surface. Density functional theory analyses reveal that the Pd exsolution significantly improve ethane adsorption capacity, thereby reducing activation resistance and boosting catalytic performance. When used as an anode for an SDC electrolyte-supported SOFC, superior performance is achieved with the peak power densities (PPDs) of 702 and 377 mW cm^-2 at 800 °C when using hydrogen and almost dry ethane (3% H₂O) as fuel, respectively. Moreover, the cell exhibits a stable continuous operation over 90 h under almost dry ethane atmosphere at 178 mA cm⁻², presenting a promising pathway for developing high-performance, nickel-free SOFC anodes that simplify system design and improves efficiency when operating with hydrocarbon fuels, thus holding significant potential for practical SOFC applications.

{"title":"Boosting direct-ethane solid oxide fuel cell efficiency with anchored palladium nanoparticles on perovskite-based anode","authors":"Shuo Zhai , Junyu Cai , Idris Temitope Bello , Xi Chen , Na Yu , Rubao Zhao , Xingke Cai , Yunhong Jiang , Meng Ni , Heping Xie","doi":"10.1016/j.adapen.2025.100206","DOIUrl":"10.1016/j.adapen.2025.100206","url":null,"abstract":"<div><div>An efficient anode catalyst for hydrocarbon fuel in Solid Oxide Fuel Cells (SOFC) should possess a stable phase structure, high catalytic efficiency, and excellent coke resistance. However, traditional nickel-based anodes necessitate high steam-to-carbon ratios to prevent coking, complicating system design and reducing the overall performance. In this work, we report a nickel-free PrBaFe<sub>1.9</sub>Pd<sub>0.1</sub>O<sub>5+δ</sub> perovskite as anode material for direct ethane SOFC, which demonstrates superior electroactivity and chemical stability. Under a reducing atmosphere, Pd nano-catalysts exsolved in-situ are uniformly anchored to the perovskite surface. Density functional theory analyses reveal that the Pd exsolution significantly improve ethane adsorption capacity, thereby reducing activation resistance and boosting catalytic performance. When used as an anode for an SDC electrolyte-supported SOFC, superior performance is achieved with the peak power densities (PPDs) of 702 and 377 mW cm<sup>-2</sup> at 800 °C when using hydrogen and almost dry ethane (3% H<sub>2</sub>O) as fuel, respectively. Moreover, the cell exhibits a stable continuous operation over 90 h under almost dry ethane atmosphere at 178 mA cm<sup>−2</sup>, presenting a promising pathway for developing high-performance, nickel-free SOFC anodes that simplify system design and improves efficiency when operating with hydrocarbon fuels, thus holding significant potential for practical SOFC applications.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100206"},"PeriodicalIF":13.0,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of scalable and privacy-preserving multi-agent frameworks for distributed energy resources

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2024-12-31 DOI: 10.1016/j.adapen.2024.100205

Xiang Huo , Hao Huang , Katherine R. Davis , H. Vincent Poor , Mingxi Liu

Distributed energy resources (DERs) are gaining prominence due to their advantages in improving energy efficiency, reducing carbon emissions, and enhancing grid resilience. Despite the increasing deployment, the potential of DERs has yet to be fully explored and exploited. A fundamental question restrains the management of numerous DERs in large-scale power systems, “How should DER data be securely processed and DER operations be efficiently optimized?” To address this question, this paper considers two critical issues, namely privacy for processing DER data and scalability in optimizing DER operations, then surveys existing and emerging solutions from a multi-agent framework perspective. In the context of scalability, this paper reviews state-of-the-art research that relies on parallel control, optimization, and learning within distributed and/or decentralized information exchange structures, while in the context of privacy, it identifies privacy preservation measures that can be synthesized into the aforementioned scalable structures. Despite research advances in these areas, challenges remain because these highly interdisciplinary studies blend a wide variety of scalable computing architectures and privacy preservation techniques from different fields, making them difficult to adapt in practice. To mitigate this issue, this paper provides a holistic review of trending strategies that orchestrate privacy and scalability for large-scale power system operations from a multi-agent perspective, particularly for DER control problems. Furthermore, this review extrapolates new approaches for future scalable, privacy-aware, and cybersecure pathways to unlock the full potential of DERs through controlling, optimizing, and learning generic multi-agent-based cyber–physical systems.

{"title":"A review of scalable and privacy-preserving multi-agent frameworks for distributed energy resources","authors":"Xiang Huo , Hao Huang , Katherine R. Davis , H. Vincent Poor , Mingxi Liu","doi":"10.1016/j.adapen.2024.100205","DOIUrl":"10.1016/j.adapen.2024.100205","url":null,"abstract":"<div><div>Distributed energy resources (DERs) are gaining prominence due to their advantages in improving energy efficiency, reducing carbon emissions, and enhancing grid resilience. Despite the increasing deployment, the potential of DERs has yet to be fully explored and exploited. A fundamental question restrains the management of numerous DERs in large-scale power systems, “<em>How should DER data be securely processed and DER operations be efficiently optimized?</em>” To address this question, this paper considers two critical issues, namely <em>privacy</em> for <em>processing DER data</em> and <em>scalability</em> in <em>optimizing DER operations</em>, then surveys existing and emerging solutions from a multi-agent framework perspective. In the context of scalability, this paper reviews state-of-the-art research that relies on parallel control, optimization, and learning within distributed and/or decentralized information exchange structures, while in the context of privacy, it identifies privacy preservation measures that can be synthesized into the aforementioned scalable structures. Despite research advances in these areas, challenges remain because these highly interdisciplinary studies blend a wide variety of scalable computing architectures and privacy preservation techniques from different fields, making them difficult to adapt in practice. To mitigate this issue, this paper provides a holistic review of trending strategies that orchestrate privacy and scalability for large-scale power system operations from a multi-agent perspective, particularly for DER control problems. Furthermore, this review extrapolates new approaches for future scalable, privacy-aware, and cybersecure pathways to unlock the full potential of DERs through controlling, optimizing, and learning generic multi-agent-based cyber–physical systems.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100205"},"PeriodicalIF":13.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0